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Machine learning approaches for predicting health risk of cyanobacterial blooms in Northern European Lakes
(2020)Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail ... -
Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status
(2020)Background: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied ... -
Machine learning for all: A more robust federated learning framework
(2019)Machine learning and especially deep learning are appropriate for solving multiple problems in various domains. Training such models though, demands significant processing power and requires large data-sets. Federated ... -
Machine Learning for Hardware Trojan Detection: A Review
(2019)Every year, the rate at which technology is applied on areas of our everyday life is increasing at a steady pace. This rapid development drives the technology companies to design and fabricate their integrated circuits ... -
Machine learning for rhabdomyosarcoma histopathology
(2022)Correctly diagnosing a rare childhood cancer such as sarcoma can be critical to assigning the correct treatment regimen. With a finite number of pathologists worldwide specializing in pediatric/young adult sarcoma ... -
Machine Learning in Meningioma MRI: Past to Present. A Narrative Review
(2022)Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it ... -
A machine learning pipeline for predicting joint space narrowing in knee osteoarthritis patients
(2020)Osteoarthritis is the common form of arthritis in the knee (KOA). It is identified as one of the main causes of pain leading even to disability. To exploit the continuous increase in medical data concerning KOA, various ... -
Machine Learning Prediction of Hypoglycemia and Hyperglycemia from Electronic Health Records: Algorithm Development and Validation
(2022)Background: Acute blood glucose (BG) decompensations (hypoglycemia and hyperglycemia) represent a frequent and significant risk for inpatients and adversely affect patient outcomes and safety. The increasing need for BG ... -
Machine learning product key performance indicators and alignment to model evaluation
(2021)Machine Learning has seen amazing progress the past years with increasing commercial use from industries across the business spectrum. Businesses strive for alignment of vision and mission statement to the actual products ... -
Machine learning symbolic equations for diffusion with physics-based descriptions
(2022)This work incorporates symbolic regression to propose simple and accurate expressions that fit to material datasets. The incorporation of symbolic regression in physical sciences opens the way to replace "black-box"machine ... -
Machine learning technique in time series prediction of gross domestic product
(2017)Artificial intelligence is gaining ground the last years in many scientific sectors with the development of new machine learning techniques. In this research, a machine learning methodology is proposed in the Gross Domestic ... -
Machine learning techniques for fluid flows at the nanoscale
(2021)Simulations of fluid flows at the nanoscale feature massive data production and machine learning (ML) techniques have been developed during recent years to leverage them, presenting unique results. This work facilitates ... -
MACHINE LEARNING to DEVELOP A MODEL THAT PREDICTS EARLY IMPENDING SEPSIS in NEUROSURGICAL PATIENTS
(2022)Sepsis is currently defined as a "life-threatening organ dysfunction caused by a dysregulated host response to infection". The early detection and prediction of sepsis is a challenging task, with significant potential gains ... -
A Machine Learning workflow for Diagnosis of Knee Osteoarthritis with a focus on post-hoc explainability
(2020)Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute ... -
A machine-learning clustering approach for intrusion detection to IoT devices
(2019)Nowadays we see the sharp increase in smart devices on the internet and in the network of things. An ever increasing problem with these devices is their protection against malware and internet attacks because of their ... -
Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases
(2022)During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community’s focus has recently shifted towards the ... -
Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke
(2021)Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We aimed to develop a machine-learning-derived prognostic model for the prediction of ... -
Macroculture, sports and democracy in classical Greece
(2013)In the present essay we examine whether and how sports affected the emergence of democracy as a political phenomenon in Classical Greece. To achieve this we introduce in a model the concept of macroculture as a complex of ...